All posts by Ashly.Arndt@experian.com

Stop reacting to Metro 2 rejections. Learn how shifting left with preventive controls blocks errors early, cuts dispute rates, and ensures bureau compliance.

In today’s economy, data fuels every strategic decision. Poor data quality can lead to costly mistakes, inefficient operations, and missed opportunities — which can be a liability. Experian empowers you to trust your data at every stage. We help businesses manage, cleanse, and connect their information with confidence. The result? Smarter decisions, stronger customer experiences, and measurable growth. Here’s what makes Experian different. 1. We know data Experian is a trusted leader in data quality, analytics, and decision intelligence. For years, leading organizations have relied on Experian to help them turn raw data into meaningful insights that drive smarter decisions and stronger customer relationships. Our approach is built on a deep understanding of how data flows through your business today and into the future — from customer onboarding and marketing to compliance and analytics. 2. We simplify complexity Data lives in many places — from customer relationship management (CRM) to marketing tools and internal databases. Experian’s suite of data management solutions brings all of this information together. Whether it’s: Cleansing to remove inaccuracies Matching to eliminate duplicates Profiling to understand data quality Enriching to add valuable insights Aperture Data Studio – Experian’s unified data quality solution – curates accurate customer data views into a single platform for data management. 3. We delivery accuracy Our data management solutions are built on powerful technology, proven methodologies, and high-quality reference data that work together to keep your information complete and dependable. Through advanced validation, enrichment, and continuous monitoring, Experian helps organizations maintain data they can trust — to drive improved customer experiences and smarter business decisions. 4. We scale with you Whether you’re managing millions of customer records or integrating data across multiple systems, Experian provides the flexibility and performance needed to keep information accurate and accessible at scale. Our cloud-based platforms and powerful API integrations make implementation seamless, so teams can focus on driving outcomes — not maintaining systems. 5. We drive results Take IKEA as a concrete example. By using Experian’s real-time address validation and enrichment solutions, IKEA increased their match rate from 83% (with a competitor) to 95%, enabling better segmentation and more effective marketing. That uplift translated into more confident targeting, improved campaign ROI, and a better customer experience. This is the kind of result we aim for with every engagement—across retail, finance, healthcare, and beyond. Your data journey starts here Data management isn’t a one-time project — it’s an ongoing journey. Experian is more than a solution provider; we’re a long-term partner in your success. With dedicated support, expert consultation, and continuous innovation, Experian helps your organization evolve with data — not fall behind it. The bottom line: Experian turns your data into a competitive advantage In a world where data drives everything, Experian gives you the clarity and confidence to move forward. From data accuracy and enrichment to governance and compliance, Experian provides the tools and expertise you need to make data your most valuable asset.

What’s the difference between a ZIP code and a postal code? Learn how they work and why address accuracy matters.

Stop losing revenue to bad data. Discover a 6-step framework to define, validate, and scale your lead generation data quality for faster sales outreach.

Improve accuracy, boost customer satisfaction, and save time and money with real-time USPS-verified address data.

Anyone can set a goal of saving money, but it is impossible to reach that goal if you don’t know when to put money away, don’t monitor your spending, or never review your bank statements. Any time you set a goal, monitoring your path to success is crucial to achieving it, and achieving effective data governance is no different. Establishing metrics and performance indicators allows you to measure progress and adjust your data governance strategy as necessary to maximize success. Once you establish proper data governance metrics and KPIs based on your business goals, you can take your data governance strategy and success to the next level. What are data governance metrics and KPIs? Data governance is the process used to ensure data follows strict rules and regulations as it enters a database and is used throughout an organization. Data governance metrics are indicators put in place in order to measure the effectiveness of an organization’s data governance processes. Key performance indicators (KPIs) provide additional ways to measure progress.Data governance centers around three key areas—people, process, and technology. Because data governance is a comprehensive process and is often more difficult to measure, organizations need to establish metrics in each of the three areas to experience success.Effective data governance strategies are crucial for organizations to stay in compliance with regulations, protect consumer data, and ensure that data is reliable for gaining insights and informing future business strategy. By using data governance metrics, organizations and businesses can ensure they remain in compliance and maintain quality, valuable data. Why are data governance metrics important? Data governance metrics and key performance indicators (KPIs) are vital for measuring success and monitoring progress. Measuring progress in data governance is important for determining whether or not your current data governance practices are effective and how to improve them if necessary.When you focus on KPIs for governance and compliance, you are able to determine how effective your processes are and how they may need to be adapted over time. After all, setting a strategy and hoping for the best is not a recipe for success, as you can never address issues that you are not aware of.Data governance metrics are powerful tools for any data-driven business, as they allow businesses to:  Highlight the effectiveness of a data governance strategy on areas like data quality and accuracy Demonstrate the success of data governance initiatives to stakeholders Uncover areas of improvement in your data governance and data management strategies Identify the need for a change of approach or priorities in your data governance initiatives Without focusing on data governance metrics, it can be easy for your best processes and strategies to fall by the wayside. Above all else, metrics and key performance indicators help provide accountability and ensure that your business maintains strong standards in its data governance procedures.  How to measure success in data governance One of the first steps in measuring success in data governance is determining what success looks like for your business. To do so, you must consider your business objectives, what operations data governance will improve, the desired data governance outcomes, and what stakeholders stand to benefit. Generally, to reach success in data governance, you should focus on four key areas to measure your business’s effectiveness in getting there. Data quality Data quality is a crucial part of any data governance strategy, and fortunately, it is not difficult to measure. Strong data quality is a direct consequence of effective data governance and data management. Some measurable aspects of data quality include: Data accuracy Data completeness Data validity Data integrity The easiest way to track your data quality is through the use of a data quality platform, which can provide a breakdown of your data’s accuracy and completeness through a dashboard and highlight areas of improvement. You can also improve data quality by using tools like email, address, and phone number verification. Each of these tools can be used in real-time or through batch cleansing methods to prevent errant data from entering your database and remove the errors that sneak in over time. People People are one of the driving forces of proper data governance in your organization. Your organization should consider who is accountable for managing data and what resources they need to succeed. For many businesses, this involves establishing set roles and teams. Some of the measurable components in this area include: How many hours of training stakeholders receive The percentage of employees trained on data literacy The percentage of employees trained on data governance strategies The number of stakeholders trained in a given time period The percentage of employees that engage with data management meetings and events Establishing these metrics allows your organization to ensure that data management teams are adequately prepared to handle tasks surrounding data governance. Process The processes within data governance help describe how your organization approaches the strategy. Processes reflect how data is collected, moved, stored, accessed, and secured. Establishing effective processes is vital for ensuring that data remains accurate and valuable throughout its life in an organization.  Measurable metrics and KPIs for your business include: The number of passed data audits carried out The number or percentage of areas and standards where your business is compliant The number of employee logins to a data insights tool or dashboard Technology Once your organization has the right systems and a strong team in place, technology is what really seals the deal. Data governance technology helps to support the process in several ways, saving time and energy for the teams responsible for managing and maintaining its quality. Data governance tools can: Assist with data profiling Identify and correct errors in data entries Assess the validity of data that enters a database Securely store and maintain data Seamlessly collect and transfer data throughout the organization Given the potential functions of data governance technology, some measurable metrics in this area can include: How often you run batch cleansing processes The number of attributes profiled The number of data entries corrected by a tool The percentage of accurate data in a database Using data governance metrics to monitor and assess the effectiveness of your technology makes it easier to determine whether your tools are achieving the desired outcomes. If the metrics are not providing the returns you would hope, then you can consider if there are better ways to use the tools or if you simply need better tools. Develop your data governance metrics for sustained success It's one thing to take strides in your data governance processes, but it is another thing to make sure that these strides are actually effective. Data governance metrics and KPIs play an important role for any data-driven business by ensuring that a data governance program is effective and achieving the desired outcomes. Establishing a strong data governance strategy is even easier with the right tools, and Experian can help. Our data quality solutions are geared toward helping you get the most out of your data, from ensuring its accuracy and completeness to gaining valuable insights and analysis from it. Contact us to learn more about how to take your data governance strategies to the next level.

Data quality errors have several consequences for a business, affecting decision-making, harming customer relationships, delegitimizing marketing campaigns and more. By addressing flaws and inconsistencies in your business’s data, you increase your ability to analyze information and make informed business decisions. Data quality issues can also lead to stressful situations. “Our critical real-time e-commerce site crashed last night! What happened?!” Well, upon further analysis, it seems that the website was expecting alphabetic characters in the comment field, which unfortunately had an unreadable “TAB” character in it that caused a cascading system failure. And even though it’s boring for the average person to think about “TAB” characters being significant enough to crash a website, these are the types of issues that data quality people get a kick out of, and that business leaders and senior managers should be terrified are going to impact their businesses. At this point, we’ve determined that a minor data quality issue has the capacity to take down a business, at least for a period of time. Now the question is, “how do these data quality issues occur in the first place?” Let’s take a look at some typical data quality problems. Why do I have data quality issues? A dataset can develop a large variety of issues over time. Unfortunately, poor quality data is somewhat inevitable to a large extent. A significant portion of issues that affect data quality occur during the data collection and entry process. This can be due to problems with your data collection system or the person entering the data. Other issues can develop over time as formatting requirements change or customer information changes, affecting your current database. However, with a data entry and management plan and the right tools, your business can easily address and correct the issues that do arise. Most common data quality issues From mistakes at the time of collection to old, out-of-date information, there are several common issues that can affect the quality of your data. Data quality issues are almost inevitable, but they are preventable, making it all the more important to keep an eye out for these issues and develop systems to address them. When collecting data and maintaining a database for your business, the following are the most common data quality issues that arise. 1. Incomplete data fields During the data entry process, it can be easy to rush through a form, overlook a few questions or simply choose not to answer some. Incomplete data leads to incomplete reports and prevents your business from gaining a complete picture of your customer information and drawing accurate conclusions from the information.Fortunately, this issue is pretty easy to address by using software that allows you to set required fields. With this software, a form cannot be submitted unless all of the data is complete. This issue can also be remedied by adding rules to forms and questions. These rules include excluding special characters, only allowing digits and using fields specifically designed for currency or dates, all depending on the question. These methods provide a great example of taking proactive steps to improve data quality before it even enters the database. 2. Duplicate data Duplicate data is one of the most prevalent data quality issues that affect businesses. For many businesses, duplicate data is unavoidable, especially when they use multiple data collection systems and methods. With a high influx of data from in-person interactions, phone calls and online forms, duplicate data is bound to happen, which makes it important to have a system in place that constantly checks for duplicate data in a database. Duplicate data also often happens when existing customer information changes. For example, it is common for a customer to provide information such as an email address to locate their account. If their email address has changed since the last time they logged in and it is not recognized by the system, then they may end up creating an entirely new account instead of changing the email address on file. To address duplicate data, your business should invest in a tool that cleanses and combines duplicated records. With the high intake of data that your business experiences, this issue is nearly impossible to fix manually and would take an unrealistic amount of time. 3. Inconsistent formatting Dates, addresses, and numbers all lead to formatting issues that can render large amounts of data useless and unhelpful. If the date is entered manually (like a request for date of birth), it can be input in any number of formats: two-digit months and days, one-digit months and days, two-digit years, four-digit years, and a mixture of each, sometimes separated by spaces, or hyphens, or slashes. And what about when someone uses an “O” instead of a zero or an “I” instead of a one? People may even spell out the date in total, like “January 1st, 2017”, which is ripe for misspellings and non-conformity. Numbers are not quite as complicated as dates but still fall into some of the same traps. The most common issue is letters representing numbers (the aforementioned “I” for “1” and “O” for “0” and the occasional heavy-metal data entry person using an “E” for a “3”). But you also have spaces being used in numeric fields, people entering “seven” instead of the digit “7.” Addresses are also affected, as some entries may place the zip code in different areas of the address. Inconsistent formatting affects your ability to run reports, analyze data and effectively compare data entries. With the number of formatting issues that can arise, it is crucial to regularly assess and cleans data. Fortunately, data cleansing tools, like address validation tools, target and correct problems with formatting to allow for consistency and better analysis. 4. Human error People filling out forms is one of the most common causes of data quality issues. It is not necessarily anyone’s fault, as human error is a natural component of the data entry process, but it is a crucial issue. Technology is helpful in reducing the impact of human error, but individuals still play a key role in the process. Common errors include typos and entering information into the wrong field, like putting a name in the address field. Other errors include willingly entering incorrect information in a field to bypass the required fields and submit the form. Although these errors are likely to happen, there are still measures to take to reduce and correct them. This makes training an important element of any data collection plan. If the person in charge of entering data is not entirely comfortable and proficient with your data management system, errors are far more likely. Well-trained data entry personnel will still make mistakes, which is why proper data validation and cleansing technology is helpful in catching and flagging errors that do occur. Having the right tools for the data entry process will help prevent poor quality data from ever entering the database. 5. Different languages and units of measurement Globalization has largely affected how we treat and work with data. It requires a more careful entry process. For businesses with customers and data entry specialists in multiple countries, the potential for entering a different language or measurement unit raises greatly, making it crucial that each system has clearly defined measurement units and a way to flag potential errors. A lack of attention to detail can particularly affect inventory ordering. A mistake in units can lead to a disastrous situation of not enough or too much of an ingredient or product. Altogether, businesses must set consistent data quality standards that account for weights, lengths, distances and currencies. How to fix data quality issues Data quality issues are guaranteed to arise at some point, especially when your business frequently gathers new data about customers or maintains a database for an extended period of time. Fortunately, there are plenty of resources to assist you with your data collection and management. Whether you are looking to avoid errors during the data entry process or cleanse data from already existing lists, Experian Data Quality can help. To learn more about how to resolve your data quality issues, contact Experian Data Quality today. We have a variety of tools, from our phone verification tools to our address validation tools, to help your business target and correct poor-quality data. Try our tools today to instantly improve your data collection and management strategies so that you can avoid data quality issues and advance decision-making on important business practices.

Data is one of the most valuable assets a business has. Organizations rely on accurate, trustworthy information to understand customers, drive operational efficiency, support compliance efforts, and make informed decisions. When data is incomplete, outdated, duplicated, or inaccurate, it can create obstacles across the business—from missed customer opportunities to inefficient processes and flawed decision-making. By understanding the causes and consequences of poor data quality, organizations can take proactive steps to strengthen their data foundations and support long-term growth. What does it mean to have poor data quality? Data may be defined differently across organizations and industries. One source defines bad data first, saying, “We define bad data as those acquired through erroneous or sufficiently low-quality collection methods, study designs, or sampling techniques, such that their use to address a particular scientific question is scientifically unjustifiable”. Examples of poor data quality in business include outdated customer contact information, improperly formatted address data, and customer data with typos. The impact of poor data quality extends to all departments within a business. For example, if you have bad customer data such as duplicate records or inaccurate records, it can affect the finance team for billing purposes, the renewals team for identifying who their customers are, and operations for processing and reporting on accurate products that the business has sold into. What causes poor data quality? Unfortunately, no business is immune from poor data quality. In fact, without a consistent data strategy, it is virtually inevitable. By knowing what to look for, you can target the precursors for poor data quality before they become deeper issues. 1. Inconsistent data collection methods If your business has not spent ample time ensuring that the inputs coming into your CRM or invoicing system are uniform, then you risk inaccurate information through these non-uniformed inputs. Establishing standard processes across all data-entry points is a great place to start; this will ensure that the data coming in is trusted, consistently formatted, and accurate, saving time and resources for your employees. 2. Ineffective data management The lack of best practices and policies could negatively impact your data consumption and management. If there are various ways to define, view, and manage data across your business—with no standard in place—you could be creating a roadblock when it comes to sharing insights across departments and overall decision-making. When it comes time for processes like data migration and integration, these standards only become foggier, making data less reliable with inconsistent formatting and quality. Your team should have a clear understanding of what data management looks like and how to work with data to meet business standards and goals. Sixty-two percent of businesses believe that a lack of basic data literacy skills impacts the value they get from their investment and technology, according to our latest study. After you standardize your records, we highly recommend that your business is trained across all departments on how to read, write, and argue with data. 3. Outdated data Understanding how to use data does not matter if the data is no longer useful. Data should be refreshed and viewed on a regular cadence to aptly take actions or make pivots as a business as needed. If your business is only collecting data and not reviewing it, then emails and addresses are becoming outdated and useless, leading to missed communication with customers and flawed decision-making down the line. Consequences of poor quality data Collecting data is not just for your convenience. Data collection should have a direct impact on business decisions and facilitate customer interaction. As a result, data quality issues can have debilitating effects, like the following consequences. Reduced efficiency Poor data quality can negatively impact the timeliness of your data consumption and decision-making. In fact, poor data quality may cost the US economy as much as $3 trillion in GDP. The best way to leverage data in a timely manner is to utilize tools alongside your process to create efficiencies with your time and resources, which will allow the expansion of timely strategy and tactics throughout your fiscal year. Without them, you are wasting time and energy that could be spent making decisions that support business growth on managing data quality issues and correcting avoidable mistakes. Missed opportunities Data quality issues affect communication between your business and the customer. If you do not have accurate contact information for your customers, then you cannot reach them to facilitate conversions. Poor data quality also causes you to miss opportunities to gain customer trust. Improper data leads to inefficiency in customer service interactions, impersonal emails, and ultimately unsatisfied customers. Reduced revenue Poor data quality can lead directly to flawed analysis and lost revenue, which is not uncommon. For example, marketing campaigns or analysis based on faulty data means not reaching potential customers and missing out on conversions. If you are investing money in failed mailing campaigns, you are also directly losing revenue. Not to mention, ineffective data management processes can also lead to a failure to follow important regulatory requirements and result in direct fines. How to improve your data quality The benefits of good-quality data can be felt across all departments. Standardized data, processes, and tools give your people the confidence and trust in the data and insight they need to make the best timely decisions for the business. To improve your data quality, you need to invest in and maintain systems that support these procedures. Follow consistent collection methods The worst way to maintain high-quality data is to start off with poor-quality data. Therefore, you need systems in place at the point of collection to prevent faulty data from ever entering your database to begin with. Resources like real-time email verification ensure that customer data is valid and accurate as soon as you receive it, preventing you from having to correct mistakes down the line. Use effective data quality tools Many tools on the market can help establish efficient processes to ensure high-quality data is captured and managed. Data validation and data management solutions are two options to ensure that both sides of the coin, data collection and ongoing maintenance, are reflected within your business. Whether you are looking for data migration, data cleansing and standardization, or enrichment with additional consumer attributes, Experian’s data management solutions could provide the tools you need to maintain high data quality and reach your goals. Take charge of your data quality with Experian The impact of poor data quality can be seen throughout multiple daily business operations, from unsatisfied customers to poor decision-making. In a data-driven world where returns matter, these are risks your business cannot afford to take. Fortunately, with an entire suite of data quality management tools, Experian can help you determine which ones can support your individualized business needs. For more information on Experian’s data management solutions, contact a consultant today!

We’re finding great value in AI as are other businesses. Here is how Experian Data Quality is using AI in our product suite today, so you can continue to innovate and automate your business. AI is a term that’s grown in importance since generative AI solutions like ChatGPT, Bard, and others, became available earlier this year. Their success at demonstrating the capabilities of large language models to understand written and spoken prompts, their ability to search and summarize volumes of content, and their proficiency at providing well written, holistic answers as well as a range of novel and predictive capabilities, has brought widespread attention. So, it’s natural to ask, “how will artificial intelligence impact the field of data quality?” What's the difference between AI, generative-AI, and machine learning (ML)? First some definition of terms. I asked (Google’s AI chat bot) Bard to answer this question. This is what Bard said: These three terms are all related to the field of computer science, but they have distinct meanings: Artificial Intelligence (AI): A broad term encompassing all efforts to create intelligent machines capable of performing tasks typically requiring human intelligence. Focus: Replicating human-like intelligence and behavior in machines. Examples: Robotics, natural language processing, computer vision, decision-making systems. Generative AI (Gen-AI): A subset of AI Focus: Creating AI systems that can generate new content, such as text, images, music, or code. Methods: Often uses machine learning techniques, especially deep learning, and large language models (LLMs). Examples: Text generation tools, image editors, music composers, code generators. Machine Learning (ML): A subset of AI Refers to the techniques used to enable machines to learn from data and improve their performance on a specific task without being explicitly programmed.Methods: Algorithms that identify patterns and trends in data, then make predictions or decisions based on those patterns.Examples: Image recognition, spam filtering, recommendation systems, self-driving cars.” Here's a useful example: AI: Imagine a solution that can evaluate a dataset and fix any data quality issues for entered values without human intervention. Gen-AI: Imagine a solution that can analyze the data and predict values for entries that are missing or incomplete. Machine Learning: Imagine a solution that analyzes the outputs from the above processes, accepts input as to the validity or otherwise of the choices made and learns how to improve on them to make the data ever more accurate and complete over time. That’s the kind of prospect that AI holds for data quality tools in the future. And apart from data quality tools, it’s also very important that organizations create a foundation of quality data to inform, train, and feed the wide range of AI tools that will likely be used across their own businesses going forward. How does Experian Data Quality use AI today? Our AI-powered data quality platform, Aperture Data Studio, automates and operationalizes data quality for businesses. Experian's Aperture Data Studio (also known as Data Studio) solution uses AI to provide a self-service data quality and enrichment platform that enables organizations to efficiently manage data quality and create an accurate, trusted, and holistic view of their information. This AI-powered platform provides a range of features such as data profiling, data cleansing, data matching, data enrichment, and data monitoring. The platform also offers real-time data validation and address verification. Over the last few years, we have put a great deal of automation into Data Studio and have received positive feedback from the analyst community indicating that Experian has some of the most advanced uses of automation on the market. Leveraging AI and ML, automation is being built into Data Studio in nearly every area: data onboarding, data discovery, issue discovery and resolution, rule creation, matching, and data observability. This automation makes Data Studio far easier to use and helps our clients reach value faster with fewer resources. Take rule creation, for example. Data analysts need to discover, document, execute, and maintain complex sets of rules across different datasets and domains to be able to keep their data fit for purpose. Data Studio incorporates machine learning algorithms for automatic data tagging that support the easy discovery and deployment of such rules, enabling them to be stored, shared, and executed, all via a business-friendly interface. Automation is also present in Data Studio’s smart profiling capability, allowing users to automatically find data issues and receive suggestions on how to resolve inaccuracies. Leveraging auto-tagging and smart profiling, the Suggest Transformation option analyzes values in the data and recommends functions to improve data consistency, clearly explaining what each transformation will do to the data to preserve data integrity. Examples are Trim and Compact which remove unnecessary space characters or convert null to zero for numeric columns containing both. Also Hash, which obfuscates sensitive data so that it can be safely saved and shared. Once accepted, transformations are easily deployed in just a couple of clicks. Other areas where machine-learning is used within Data Studio include: Powerful outlier analysis to proactively detect and inform users of unknown and known anomalies within the data. Observability features provide automatic data monitoring to detect interesting or unexpected changes to the data. Tuned matching rules for optimized accuracy when comparing records from different sources. Smarter merge suggestions when configuring how best to deduplicate records with duplicated data. The Aperture Data Studio Roadmap indicates that further investment in AI is already under investigation. The goal is to determine how can Gen-AI natural language processing (NLP) models be used to increase user efficiency and improve collaboration through personalized experiences and AI-driven intelligent suggestions. A robust data governance and data quality strategy is the prerequisite to AI business success The early adopters of AI, ML, and Gen-AI were primarily organizations with robust data and analytics strategies. Now, as the hype continues, more organizations without that foundation are keen to take advantage of the new innovations. Many analysts advise them that they can’t get started without building a strong data strategy. One big challenge is the breadth of information used to inform publicly available Gen-AI solutions. For example, today’s open GPT-based solutions such as Bard, Bing365, and OpenAI are trained on a broad spectrum of internet and social media data. Any frequent user will know that this often results in “hallucinations” where, to collaborate and simply provide an answer, the solution will misinterpret the data and present a totally incorrect result as the truth. Without human intervention and understanding, such “hallucinations” can cause significant misdirection and even harm. The answer for businesses interested in using Gen-AI in their own products and decision-making is to narrow the input data to information that is relevant to the purpose and to make sure that the data is as accurate as possible. Without accuracy, the models can still produce hallucinations. Without trust, the resultant decisions will not be acted upon or acted upon slowly, after the wisdom of the decision has been thoroughly vetted. The latter course, eliminating much of the business value assumed for the AI solution. Success is going to require a strong blend of data quality, data governance, and data security. Data quality ensures that the “training” data is accurate, complete, and comprehensive. Data governance manages the data quality and accessibility, determines ownership, and carefully catalogs and defines the information available so that decisions can be made about the best data to use. Information security will be needed to protect the data from being shared inappropriately or being purposely corrupted to impact competitiveness or reputation. A key example where governance, quality, and security could make an impact is in the call center. One use of Gen-AI is in call center applications where bots use customer data to efficiently respond to personalized customer questions. The benefits of improved customer satisfaction and efficiency should be significant, but if the customer data gets corrupted or is simply wrong, the opposite effects will likely occur. That is, poor satisfaction and less efficiency as the firm tries to do damage control. The challenge for many firms is that the traditional, top-down approach to data governance is too expensive and unwieldy. It can take years for a business to mature enough to adopt a data governance program—and data quality often takes a backseat due to lack of ownership. Now, more agile firms are taking a bottom-up approach and seeing success. Agile firms taking a bottom-up approach are building their data governance and quality programs one step and one issue at a time. Perhaps, leaders bring governance practices to the data analytics department first then expand involvement to other departments as issues arise and are solved. Over time, those with a vested interest in solving their department’s problems will become involved and take ownership, broadening the organic adoption of governance and quality across the business. Aperture Data Studio and data governance Experian has partnered with leading data governance vendors, such as Alation, to provide bi-directional interfaces to applications. Such integrated interfaces allow the governance solutions to take advantage of profiling, monitoring, and other data quality capabilities while providing Data Studio access to a wide range of metadata to increase its operational effectiveness. The net result for Experian and Alation joint customers is a far more robust data quality and data governance capability. Experian continues to invest in data governance for Aperture Data Studio customers by expanding partnerships and integrations with companies like IntoZetta, who is a UK-based software company that specializes in data governance, quality, and migrations for specific industry sectors. By further participating in the data governance market, Experian is focused on providing our customers with a well-rounded tool set that helps businesses innovate their use of artificial intelligence with a strong foundation of data quality, governance, and security.

In today’s business world, organizations collect customer data through several channels and maintain contact records across different databases. But what good does this customer data provide you if it’s inaccurate or out of date? Proactively ensuring you have accurate, up-to-date contact data that’s ready for use allows your organization to make agile business decisions backed by high-quality data. What is data quality? Having data that is of high data quality means the data is fit for intended uses—like improved analysis and reporting or an innovative marketing strategy. Data-driven decisions in your business are only as good as the data that guides them. With a data quality solution, you can validate, standardize, enrich, and profile your data to unlock its full potential. Why is it important to test data quality? Poor data quality can have a significant impact on business performance, making it more difficult to build customer loyalty and trust, understand customer preferences, maintain accurate communication, and execute effective marketing strategies. While ensuring your customer data is reliable may seem like a complex task, Experian’s data quality solutions simplify the process by helping you collect, validate, and maintain trusted data. We’re here to help you take the right steps to test and improve your data quality. What are the steps to data quality testing? Step 1: Define specific data quality metrics Your organization needs specific metrics to test against to understand what you are targeting and need to improve.Think about how your business uses data and what problems higher quality data can solve for. Some examples include: Amount of returned mail Number of individuals with complete contact information Number of personalized offers accepted Data quality metrics that matter will vary based on your job role or focus area. If you're part of a shipping and logistics team, you want to ensure your organization is collecting valid addresses at checkout, so you are not retroactively dealing with return packages and wasted warehouse resources. If you are an email marketer, your gauge for data quality may be how many email addresses on your list are reachable. Step 2: Conduct a test to find your baseline Driving data quality improvement throughout your organization won’t be possible unless you have defined a baseline and identified the gaps in your data that you want to improve. In our warehouse example, there are specific tools available to help you easily validate addresses before the outbound packages leave your facility. For the email marketer, our tools allow you to validate your email list data quality before launching an email campaign and watering down your engagement metrics. Step 3: Try a solution Once you have determined what business areas you need to improve your data to meet your goals, you can start addressing your specific data quality issue. In the case of the poor address data quality example, you have options, including an immediate one to fix your existing problem (batch address verification) and one that is more long term, avoiding bad addresses before they even enter your database (real-time validation at point of capture). Step 4: Assess your results After you have implemented your data quality solution, is it important to run another test against your initial metrics to understand where you saw positive improvement or to identify where you need to continue refining. The results will determine how you adjust your data quality solution. Data quality can be mean something different from one organization to the next. But as long as you are defining criteria that make sense for your business and testing against those, you can be sure you’ll be able to find ways to drive improvement. How Experian can help with data quality testing At Experian, we believe in empowering business users to better understand their data assets to transform their businesses. With Experian’s data quality tools, we provide comprehensive solutions to help your business maintain the accuracy of your customer errors, reduce errors, and avoid additional costs associated with bad data.

Are your customer addresses up-to-date? Have any of your customers moved since the last time you checked their address? Outdated, inaccurate customer address data can cost your business a lot of money and waste a lot of time. From typos when entering an address to a customer moving to a new home, it’s surprisingly easy to have incorrect address information. That’s what makes address verification so important for your business. By validating addresses, you make sure your customer data is clean and accurate. This helps your packages and mail reach their destination on time. What is address verification? Address validation is the process of checking an address for inaccuracies and fixing any problems. Address verification tools from Experian look for inaccuracies in your existing address lists. When an incorrect address is found—whether it’s due to incomplete street data or improper formatting—the system fixes the mistake. Real-time tools can also be used to check customer data as it enters your database. 4 address verification benefits Improve the customer experience: Have you ever ordered a package that arrived long after the expected delivery date? You probably didn’t order from that company again. On-time arrivals make customers happy and ready to purchase from you again. It’s important to create a good customer experience—starting with accurate addresses for on-time deliveries. Increase delivery speed: The right address formatting can potentially increase delivery speed. An address with the wrong formatting may make it to its destination, but it will likely take longer to be sorted and sent to the right distribution facilities. Address verification standardizes address formats, so shipping companies don’t have to spend as much time processing the package. Save money: Address verification saves your business money. For example, a valid address gets delivered to the correct address the first time, saving you the money you would otherwise spend on reshipping a package to the correct address. Save time: You’ll also save time when you verify your address lists. Each time a package or mail is returned to your business, you lose valuable minutes and hours updating the address and reshipping the package. By using address verification, you’ll also save time you’d spend dealing with upset customers who are wondering why they haven’t received their packages. Finally, your employees won’t have to manually update address records or delete duplicate entries. Address verification benefits in action Retail businesses: Whether you offer local deliveries or just want to send marketing mailers to the community, you’ll want to make sure you have the correct addresses for local customers. eCommerce shops: Online retailers often exclusively deliver goods through the mail. That’s why customer addresses need to be correct. You might also have customers around the globe and need international address verification to make sure you have the right formatting. With address verification, you can get packages to customers on time—whether they’re across town or the world. Business professional services: If you’re a business that works with other businesses, like a business bank or lawyer, you can use business address verification to make sure you’re sending important documents and files to the right address. Company address verification works just like residential verification using the latest business address data. Service providers: Businesses like plumbers, landscapers, and on-site computer repair companies rely on accurate addresses to get to customer appointments on time. With address verification, service business owners can feel confident knowing your crews are headed to the right location each time they make a service call. Restaurants: More and more restaurants are exploring delivery as an additional dining option for customers. Investing in real-time address verification helps you collect the correct address from the start, so your deliveries arrive fresh. Keep addresses clean and updated Having a database of clean, up-to-date addresses is important for your business. On-time, accurate deliveries and service not only help you save time and money, but also give your customers a better experience. Happy customers are often repeat customers who will recommend your business to their friends and family. Learn more about validating addresses by working with Experian’s data quality team.

In today’s hyper-connected world, maintaining clean, accurate email lists is more critical than ever. For B2B marketers, a bounced email or a deliverability error isn’t just a missed opportunity to connect—it can damage your sender reputation, waste resources, and undercut your lead-generation efforts. Enter email validation, the unsung hero of any robust marketing strategy. In this post, we’ll dive deeper into what email validation is, explore why it’s an essential part of B2B outreach, and show you why Experian’s email validation solution is the partner you need to keep your lists—snatched and thriving. What is email validation? Email validation is the process of verifying the deliverability and validity of an email address. Unlike a simple syntax check (does it have an “@” symbol?), a full email-validation workflow from Experian includes:  Syntax & format verification Ensures the address follows standard email rules—no spaces, correct placement of “@”, valid domain formatting, and more. Domain checkConfirms that the domain exists and is configured to receive email by inspecting DNS records and mail exchanger (MX) entries. Mailbox existence testReal-time check via SMTP to verify that the email address is real and active Filter spam traps and temporary email addressesFlags spam traps, emails with profanity, and temporary email addresses  By layering these checks, email validation helps your company cut operational costs, wasted time, and helps ensure they reach their customers at the right time, the first time.   Why email validation matters for B2B marketers Protect your sender reputation   Every hard bounce chips away at your domain’s credibility. With a clean list, you minimize bounce rates, keep inbox providers happy, and ensure higher deliverability for your campaigns. Reduce costs & improve ROI  Email service providers often charge based on total list sizes or messaging volumes. By removing inactive or invalid email addresses, you cut down your waste so you can better get to know your real, engaged customers.  Enhance engagement metrics  When you cut out the junk, it helps to improve your metrics to show you your true performance metrics. Open rates, click-through rates, click-to-open—are all key metrics for demonstrating program success.  Comply with data privacy regulations  GDPR, CAN-SPAM, and other regulations require you to treat personal data responsibly. Using validated, permission-based email data helps you stay on the right side of privacy laws.  Fuel better insights  A clean data foundation leads to more accurate analytics, segmentation, and personalization—driving smarter decisions and more relevant B2B interactions.  The Experian advantage for email validation When it comes to email validation, not all solutions are created equal. Here's why top B2B teams turn to Experian: Decades of trusted data expertise With 25+ years in the data quality space, Experian brings unmatched scale and accuracy to the email-validation process.  Unified, enterprise-grade platform Seamlessly integrate email validation into your existing marketing automation, CRM, or data-onboarding workflows—no siloed point solutions required.  Global coverage & compliance Validate addresses across regions and languages, all while adhering to stringent privacy-by-design principles and regulatory frameworks.  Real-time & bulk validation options Whether you need to vet single addresses at POS or cleanse massive lists in batches, Experian’s flexible API and portal allow you to validate at your pace.